Operationalizing Data Science using Cloud Foundry Alpine Data Lawrence Spracklen VPE

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Alpine Data

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Operationalization

•  What happens after the models are created? •  How does the business benefit from the

insights? •  Operationalization is frequently the weak link –  Operationalizing PowerPoint? –  Hand rolled scoring flows?

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Barriers to Model Ops

•  Scoring often performed on a different data source to training

•  Batch training versus RT/stream scoring •  How frequently are models updated? •  How is performance monitored?

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Define Act

Transform Deploy

Model

Business Leader Employees and Customers

Chorus 6

The Chorus Process

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Turn-key solutions Train models

Coordinate Govern Deploy

RESTful Scoring engines

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Pivotal BDS

•  Provides support for high-performance SQL on both Hadoop and traditional data warehouses –  HDB/HAWQ and GreenPlum

•  Alpine supports SQL & MADlib accelerated machine learning algorithms on both HAWQ and GPDB

•  Alpine models trained on HAWQ can be scored on GPDB and vice versa

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Cloud Foundry (CF)

•  Models trained on HAWQ or GPDB may not be scored against these systems –  May not use the Hadoop cluster at all

•  Need standalone scoring support –  Readily deployed, maintained and scaled to meet the

requirements of specific customers •  CF provides an elegant way to deploy scalable

scoring engines –  Across a variety of public and private clouds and datacenters

•  Require execution framework agnostic way to specify models

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PMML

•  XML based predictive model interchange format –  Created in 1998 –  Version 4.3 just released

•  Good for specifying many common model types •  Limited support for complex data preprocessing

–  Can require companion scripts/code •  Broad PMML export support •  Limited import support

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Turn-key model updates

Conditionally push model to Cloud Foundary Scoring engine

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Turnkey Model Ops 1) Launch CF scoring engine 2) Configure export 3) Score data

Curl –X POST …

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PFA

•  Portable Format for analytics is the JSON-based successor to PMML –  Version 0.8.1 available

•  Significant flexibility in encapsulating complex data pre- and post-processing

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Complex scoring flows

Push entire flow to Alpine Scoring Engine

Trained Model

Pre-processing

Scoring Flow

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PFA Support

•  Not only model operators need to export PFA •  Process entire DAG from raw data input to final

model output –  Synthetize PFA doc to represent the flow

•  PFA is capable of representing many key operations –  Much richer than PMML

•  Provides support for supplemental info to be leveraged by the scoring flows

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Conclusions

•  Operationalization of Data Science findings often overlooked

•  Need easy model deployment to ensure maximum impact

•  PFA makes it much simpler to deploy complex scoring flows

•  Pivotal + Alpine Chorus provide turn-key model operationalization support

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Questions?

lawrence@alpinenow.com

Additional information

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External interaction

Streaming RESTful Batch

Hadoop DB

Chorus

Alpine IR

JDBC

PFA/SQL/PMML

Internal External

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Model lifecycles

Sources

Stores

Models

Engines

Apps

Actions